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AI Neural Network - PLOS

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Last Updated: 12 July 2022

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Implementation of synchronization of multi-fractional-order of chaotic neural networks with a variety of multi-time-delays: Studying the effect of double encryption for text encryption

This paper discusses the possibility of double encryption, which is the synchronization of non-identical multi-order neural networks with multi-time delays and symmetric encryption. We therefore combine both methods where the encrypted keys are generated from the third layer of the neural network frameworks and used only once to encrypt and decrypt the data.

Source link: https://doi.org/10.1371/journal.pone.0270402


Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks

Extracting the objectu2019s pose from the x-ray photographs is a time-consuming and costly process, although x-ray based measurement equipment prevents the soft-tissue artefacts that appear in skin-based measurement methods, extending the object's pose from the x-ray images is a time-consuming and costly process. We trained a deep-learning simulator to determine the 6D poses for the femoral and tibial implant components based on a database of over 106 million annotated photos of knee implants gathered over the last decade with our moving fluoroscope during daily life. Our approach gives personalized predictions of the implant poses, even for unseen patients by pretraining a single stage of our architecture using renderings of the implant geometries.

Source link: https://doi.org/10.1371/journal.pone.0270596


China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model

We compare the predictive power of LSTM-HMM with other dynamic forecast tools in different time windows, which includes the Hidden Markov Model, Gaussian Mixture Model, LSTM-HMM, and LSTM-HMM with input of monthly Consumer Price Index or quarterly CPI within a 4-year, 6-year, and 10-year time window.

Source link: https://doi.org/10.1371/journal.pone.0269529


Vowel speech recognition from rat electroencephalography using long short-term memory neural network

Using a bidirectional long-term memory network and classical machine learning techniques, this research sought to identify key features that may be correlated to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single trial basis. Two male Sprague-Dawley rats were subjected to microelectrode implantation surgery to collect EEG results from the bilateral auditory fields. Five different vowel speech stimuli were selected, /a/, /e/, /i/, /o/, / A EEG obtained under randomized vowel stimulation was minimally processed and normalized by a z-core conversion, which could be used as input for speech recognition's classification. These results show that LSTM layers can effectively model sequential data, such as EEG, as shown by EEG; thus, educational functions can be developed using BiLSTM embedded with end-to-end learning without the use of additional hand-crafted feature extraction techniques.

Source link: https://doi.org/10.1371/journal.pone.0270405


Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain

Lesion inference analysis is a key method for determining the causal contributions of neural elements to brain function. Although brain perturbation inferences are scientifically correct, methodological difficulties persist. We systematically and stringently lesioned a small artificial neural network encoding a classic arcade game to elucidate these limitations. In particular, neuroscience is looking to determine which brain functions are causally involved in cognition and behavior by perturbing them. Here, we used an Artificial Neural Network as a ground-truth measure to compare the inferential capabilities of two key strategies, lesioning one component at a time rather than sampling from the database of all possible combinations of lesions. According to misleading results, lesioning one component at a time leads to misleading findings. We recommend that simulation experiments and ground-truth models be used to test the assumptions and assumptions of current brain mapping by perturbation.

Source link: https://doi.org/10.1371/journal.pcbi.1010250


Memory augmented recurrent neural networks for de-novo drug design

In addition to inferring a correlation between elements of an input series and target output, a recurrent neural network is a machine learning system that gains the ability of elements of an input series. Memory enhancements enable the RNN to find the correlations between particulars of the input over a protracted length of the input sequence. For the de-novo generation of small molecules, we are inspired by stack augmented RNN's success in creating strings for various purposes. We also compare the results of these architectures with simpler recurrent neural networks without the use of a memory component to see the effects of augmented memory in the task of de-novo generation of small molecules.

Source link: https://doi.org/10.1371/journal.pone.0269461


The contribution of object identity and configuration to scene representation in convolutional neural networks

Scene perception involves determining the identities of the objects composing a scene in connection with their configuration. How object identification and configuration data is weighted during scene processing is used and how this weighting changes throughout scene processing, however, is not fully understood. Recent advances in convolutional neural networks have demonstrated their ability at scene processing tasks and established correlations between processing in CNNs and in the human brain. Despite differences among the four CNN architectures on all CNNs, we found a common pattern in the CNN's reaction to object identification and configuration changes. During scene processing, these results are one of the first reports of how object identification and configuration details are weighted in CNNs.

Source link: https://doi.org/10.1371/journal.pone.0270667

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions